대한전자공학회:학술대회논문집 (Proceedings of the IEEK Conference)

대한전자공학회 (The Institute of Electronics and Information Engineers)
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함수 근사를 위한 점증적 서포트 벡터 학습 방법

Incremental Support Vector Learning Method for Function Approximation

  • 임채환 (고려대학교 제어계측공학과) ;
  • 박주영 (고려대학교 제어계측공학과)
  • 발행 : 2002.06.01
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초록

This paper addresses incremental learning method for regression. SVM(support vector machine) is a recently proposed learning method. In general training a support vector machine requires solving a QP (quadratic programing) problem. For very large dataset or incremental dataset, solving QP problems may be inconvenient. So this paper presents an incremental support vector learning method for function approximation problems.

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